Since the creation of a unified Department of Housing and Human Services (HHS), Boulder County, CO has been a testament to the benefits of holistic human services delivery. Through its integrated service delivery system, Boulder County has been able to expand the number of residents receiving services by 140%, focusing on front-end and early intervention measures to prevent more costly services in the future. Technology has been a key feature of this transition. The Department, as it exists today, was formed after a 2008 merger between the County’s housing and social services agencies. To support this effort, HHS developed an integrated service delivery system, including technological tools that allow employees to track clients’ case histories across programs, refer clients to additional program areas, and collaborate with other department caseworkers. Read more in "Boulder County Colorado: Integrated Service Delivery" by Sam Gill, Indi Dutta-Gupta, and Brendan Roach.

Although lead paint was banned in the United States in the 1970’s because of the harmful effects exposure can have on children in particular, there may still be older homes with remnants of the dangerous chemical. The City of Chicago Health Department and the Center for Data Science and Public Policy at the University of Chicago (DSaPP) partnered to tackle this specific issue with the use of machine learning and analytics strategies. The goal of the partnership was to help identify homes that are most likely to still contain lead-based paint hazards. The data scientists at DSaPP built statistical models that predict possible exposure based on factors like the age of the house, historical health data on children previously exposed at certain addresses, and economic conditions of neighborhoods. The data sources used by the predictive model include blood lead level tests and home inspection records, combined with a variety of publicly available data, such as census information about neighborhoods and construction data about the size and structure of individual houses. The paper detailing the project said that the model creates a list of at-risk homes with individual risk-scores and that the Chicago Department of Public Health would use this list to prioritize homes it would target with outreach and intervention to engage at-risk families and landlords.

Five years ago, New York City launched an initiative, HHS-Connect, to collect its social service data in one place. The idea is to allow clients to walk into different social service agencies without having to re-enter their information and complete duplicate paperwork. “We have a vision of a client walking into, for example, a homeless shelter and not having to reapply your information if you had already been to the public welfare office or to the Administration for Children’s Services,” said Kristin Misner, chief of staff to the deputy mayor for health and human services. Read more in "Big Data Gives a Boost to Health and Human Services" by Stephen Goldsmith.

In Philadelphia, an initiative that originally helped connect low-income seniors apply for benefits over the phone has transformed into a data-driven anti-poverty program that can enroll eligible residents in 19 different services and benefits within thirty minutes. Now called BenePhilly, the program functions as a quasi-governmental agency and has data sharing agreements with the state and city. By reducing duplicitous forms, opening in-person locations at trusted community organizations, and relying on powerful, open-source technology to assess eligibility, BenePhilly is transforming the process of applying for — and seamlessly receiving — benefits, and increasing trust in government assistance services. Read more about the program here.

Across the globe, nitrogen dioxide (NO₂) from traffic pollution causes 4 million new cases of asthma in children, according to a new study on vehicular air pollution. In response, the Environmental Defense Fund is using mobile sensors in cities like Oakland, Houston, and London to measure levels of air pollution at the block level. Mapping this information and showing how levels vary in urban areas, where different ends of the same block can have different levels of toxic pollution, helps inform policies at the city and municipal level. Recent congestion pricing proposals and low emission zones will be watched closely to see if they produce measurable changes in the pollution data.

In the span of just one year, Cincinnati managed to decrease its infant mortality rate (IMR) by over 25  percent, from 13.3 deaths per 1,000 live births in 2012 down to 9.9 in 2013. To accomplish this feat, the city has incorporated and leveraged relevant data to concentrate its efforts where they are most needed. Since 2007, this targeted undertaking has tracked various indicators and outcomes, such as mother’s zip code, race/ethnicity, mental health, and smoking habits, as well as the child’s birth spacing and sleeping environment. By using data to zero in on quantifiable risk factors and on at-risk communities, Cincinnati is making major strides on a difficult undertaking, the fight against infant mortality. Read more in "Using Data to Combat Infant Mortality in Cincinnati" by Victoria Kabak. Maryland and Indiana also have notable successes in this area.

Improved analytical capabilities allow agencies to identify and implement more effective services for clients. In Oklahoma, as part of the SoonerCare (Medicaid) program, officials analyzed patient data including comorbidity factors to identify individuals prone to poor health outcomes. Equipped with a list of at-risk Medicaid recipients, managers have worked to sign these individuals up for intensive, managed-care programs. Meanwhile, the Rhode Island Department of Children, Youth, and Families developed the Real Connections program, which analyzes data on a child’s social network. Using this analysis of existing information, the Department is able to identify mentors best suited to enable the best outcome for each child. Read more in "The Technology Opportunity for Human Services" by Sam Gill, Indi Dutta-Gupta, and Brendan Roach. Stephen Goldsmith's "The Nexus Between Data and Public Health" highlights additional examples of health data that can help policymakers improve the health of their communities.

There is not a lot of longitudinal data on the long-term outcomes of individuals who have experienced homelessness, but in 2018 the Homeless Policy Research Institute (HPRI) published data that looked at different homelessness interventions and measured "the ability of these interventions to help individuals avoid returning to homelessness." Based at the University of Southern California, HPRI focused this study on homelessness in Los Angeles. The identified several key factors that influenced whether or not individuals were likely to return to homelessness after engaging with an intervention (such as rapid rehousing or transitional housing). 

1. Relationships and strong social capital reduces episodes of homelessness. Additionally, financial and emotional support help provide significant stability.

2. Age can also predict who returns to homelessness, with older heads of households more likely to reenter emergency shelters.

3. Individuals who leave interventions under "imposed departure from services" show some of the greatest return rates (this can include those who refused to pay rent, exhibited behavioral issues, engaged in drug use, etc).

4. Exiting the system into stable life and housing situations is obviously an important predictor, as is the history of homelessness. Individuals who have experienced homelessness multiple times are more likely to enter into the system again, as opposed to individuals who are experiencing their first instance of homelessness. 

5. There is also evidence to suggest that homeless people of color have a greater risk of reentry compared to other racial/ethnic groups, but there is very little quantitative data to support the qualitative evidence.

New York City's Department of Homeless Services (DHS) has partnered with academics to develop customized risk assessment tools that support caseworkers in determining the best approach for each client during the screening process. DHS is also exploring more proactive approaches, partnering with the SumAll Foundation to analyze data on eviction notices to predict which cases are most likely to result in homelessness. While the pilot is still being tested in specific neighborhoods, the analytics will eventually become part of the City’s data visualization project that allow staff to visualize neighborhood data such as shelter entries and eviction filings, while also being able to tell caseworkers which of the thousands of households or buildings on the map are actually most at-risk of shelter entry.  By tailoring outreach efforts and reducing barriers to access, DHS can provide more services to more at-risk families. Read more in "Data-Driven Strategies for Reducing Homelessness" by Lyell Sakaue.

Additionally, research has should that nonbinary, transgender, and LGBTQ youth are one of the demographics most likely to experience homelessness. Across the country, about 40 percent of unhoused youth identify as LGBTQ. 

Data Science for Social Good partnered with the Illinois Department of Human Services to identify women at risk for having adverse births, which are associated with negative personal, financial, and developmental outcomes for both the mother and the baby. They identified risk factors, including stress, socioeconomic factors, substance use, quality of life indicators, healthcare access, and age, that could be used to predict women at high risk for adverse births. The state then provided targeted programs and assistance to these identified high-risk women, giving them needed assistance. Read more on the DSSG site. 

Data Science for Social Good and the Mesa Public School System are using Mesa's education data to identify students who are off-track for their future plans. Using students' classes, grades, test scores, and attendances, they can predict which students are college-ready but may not be applying to college, only applying to two-year programs, or will enroll but not graduate from college. These students can then be given extra support and resources to empower them to apply, graduate, and succeed. Read more on the DSSG site.

A Texas law requires public schools to record fitness data on every student. Through data-sharing agreements with the school districts, Austin-based nonprofit Children's Optimal Health (COH) gathers metrics on BMI and cardiovascular fitness scores that are geo-tagged with social and economic information. COH converts de-identified person-level data to aggregate neighborhood-level maps that illuminate the conditions faced by families and children in the area, all while protecting personal information. Enhanced with other datasets, these maps tell a more complex story of the factors that influence health outcomes — from proximity to fast food restaurants to the stress of high neighborhood crime rates. Read more in "Austin Targets Youth Obesity with Neighborhood-Level Data" by Stephen Goldsmith.

When Bloomberg Philanthropies launched the Mayors Challenge, issuing a call for the most innovative proposals by cities across the United States, Providence, RI Mayor Angel Taveras seized the opportunity to seek out a new, creative solution to a serious issue in his city. After reviewing a number of ideas, he and his team ultimately developed the program now known as Providence Talks. The team discovered research that shows that high-income children hear an average of 30 million more words than their low-income peers in the first three years. The seminal study of language environment by Betty Hart and Todd Risley showed that the amount of conversation children had with their parents by age three was positively associated with their IQ scores at that age, along with a host of other positive outcomes. Providence decided to apply this important finding by creating a city-led effort to close the word gap using innovative technology: devices that can record and allow measurement of the auditory environment of children. A preliminary study demonstrated that sharing feedback reports generated by the recording devices led caregivers to increase the number of words spoken to their children by 55 percent. Simply having the information about the number of words their children were hearing inspired them to talk more, read more, and interact more with their children. The children’s increased exposure to words was the single greatest predictor of improved language skills and learning readiness before entering school. Read more in "Providence Talks: Progress on Closing the 'Word Gap.'"

West Nile virus, an ailment once rare and relatively unknown in the United States, is now an annual danger in many suburban communities. In Suffolk County, New York, a large suburban and rural county on Long Island, officials began seeing West Nile cases in the early 2000s. The county developed a model to assess the risk of outbreak using a combination of statistical methods and geographic information systems. Through modeling, they found relationships between human West Nile cases, landscape factors, population demographics, and weather patterns. Initial results showed a complex interaction between these factors and human cases of West Nile virus. Using this hot-spot analysis, Vector Control now targets larvicide efforts in established hot-spots and uses aerial adulticide spray only where quantitative evidence supports the use of pesticides. By being strategic in the use of analytics, the agency has saved time and money, while still providing a high level of public safety. Read more in "Predictive Tools for Public Safety" by Stephen Goldsmith.

Zika is another mosquito-borne illness that is making headlines in U.S. cities. In "How U.S. Cities Can Target Zika Risk," Jon Jay analyzed data from Miami and found relationships between vacancy, poverty, and Zika outbreaks. He applied that analysis to New Orleans and Houston, suggesting that vacant housing reduction strategies could help such at-risk cities tackle the challenge of Zika.

In Miami, new business owners no longer have to endure the months-long process of in-person permitting and applications in order to found a new business. The city’s eStart initiative launched in January 2021 and is an all-digital tool that helps current and future business owners do things like apply for licenses and fill out zoning compliance forms. Users can do all of this from their phones, streamlining the process for business owners and government offices that usually dealt with duplicative forms.

A 2018 study published in JAMA Pediatrics found that retail sales of tobacco to minors is more prevalent than previously thought. Stores that are near schools are a prime location where youth are exposed to cigarette advertisements, and many cities are regulating the sale of tobacco products near school entrances. Additionally, certain pharmacies and convenience stores near gas stations have been cited by the FDA for the illegal sale and distribution of tobacco products to minors.

In Tulsa, OK, the Urban Data Pioneers program is an inventive community engagement strategy that recruits community members to investigate and address pressing issues using public data. One of the most successful civic tech projects looked at data on 911 calls to help determine the highest call volume times. From this research, the call center optimized staffing by scheduling to meet the highest call demand times.

Through a partnership between the City of Atlanta and the Data Science for Social Good (Atlanta) program, a research team worked with the Atlanta Fire Rescue Department (AFRD) to build Firebird, a framework for identifying and prioritizing fire inspections at commercial properties. Firebird determines fire risk scores for city properties, using "machine learning, geocoding, and information visualization". The predictive risk model used available AFRD data, new data on previously uncounted commercial properties, and historic fire and inspection data. This data-driven approach helps local governments identify risky properties, manage fire department resources, and mitigate commercial property hazards. 

According to Keith Chen, Associate Professor of Behavioral Economics at the University of California Los Angeles, cities should take a behaviorally-smart approach to regulating micro-mobility. Instead of broad regulations or outright bans, Chen advocates that cities make decisions that are informed by the behaviors of its residents.Cities should also utilize dynamic caps of micro-mobility vehicles and find motivating incentives for companies to align with city goals. 

Promising results from multiple 2020 pilot studies show that data can help smooth curb usage and regulate zones and pricing. Each study was slightly different, as they were tailored to the size, existing infrastructure, and flow of each city. However, each city was able to collect data that guided when and where to open loading docks, how to accommodate residential deliveries, and how to use dynamic pricing to adjust congestion and traffic. In order to regulate the curbs, pilots like these must be conducted so that city officials have data to guide their initial decisions. Continued data collection will be important, to ensure that there aren’t adverse effects and to tweak the dynamic pricing and deliveries.

Intelligent transport systems (ITS) are used worldwide to predict traffic and increase safety. Intersections are one of the most difficult places to manage with ITS, since they involve so many different types of mobility (walking, biking, driving, scooting, etc). Researchers in China and Australia developed a new approach to predicting intersection traffic using tree models. Historic and recurring events needed to be combined with "real-time spatio-temporal information related to the nonrecurring events" like construction or accidents. Using real-world data sets from Melbourne, Australia the researchers were able to show that their new methods were better at predicting intersection traffic than old models.

Back in 2011, Transport for London, the transit agency behind the London Tube, collaborated with a group from the University College of London to study the daily operations of the subway system via the familiar Oyster fare card. The result was a paper detailing how the commuting patterns of individuals coalesce into a massive, crowded network of movement on the Tube, resulting in congestion and strain at important system hubs. The smart card Oyster system allows researchers to collect data on the journeys of individual travelers (with the assurance of safeguards to protect customer privacy) to understand the complex dance of the metro system. The data visualization that the study produced allows researchers to model theeffects of various situations on congestion patterns; now planners can determine exactly what would happen if mechanical failures were to slow trains on a particular line or cause other service problems. Read more in "Streamlining the London Tube with Data" by Nick Carney.

A number of US cities have implemented the Vision Zero initiative, a campaign that aims to eliminate traffic fatalities through education, enforcement, and engineering. As a part of the initiative, San Jose partnered with the Department of Transportation (DOT) and used data analytics and GIS analysis to identify 14 priority corridors where most major injury accidents occur. In response, the city has adjusted deployment times for Traffic Enforcement Unit (TEU) officers to align more closely with peak times of traffic collisions and provided them with GIS maps of high-incident intersections. Read more in “San Jose Improves Traffic Safety With Data” by Kevin Miller.

A research study from the Massachusetts Institute of Technology compared travel behaviors for a control group and a group that received 50 percent reduced travel passes. The preliminary results showed that the group receiving the discounted cards made about 30 percent more trips than the control groups, and they took more trips for the purposes of health care and social services.

Since the transportation sector is the largest producer of greenhouse gasses in the United States, many cities are interested in using behavioral economics to encourage sustainable modes of transit like bikes and subways. Nudges are non-intrusive interventions that guide people toward a desirable action, and in this article there are several examples of effective nudges that cities around the world are using to "green" their transportation sector. 

Internet of Things technology is helping cities monitor water flow to optimize water pumping and reduce the amount of water lost to leakage. Read more in "Come Drought or High Water" by Laura Adler.

Data from a study in Philadelphia found a positive impact from the greening of urban environments. Researchers recruited test subjects to wear heart rate monitors and walk around two clusters of vacant lots; some of the lots had received a greening treatment courtesy of the Philadelphia Horticulture Society. The other lots were the control. After reviewing the GPS and heart rate data, the researchers found that walking near the greened lots decreased subjects’ heart rates, as opposed to walking in the control lots. This study helps policymakers understand the ways in which housing and public health are intertwined, and how fairly simple infrastructure improvements can effect public health determinants. 

Time, weather, and deferred maintenance have not been kind to many of New York City's East River crossings. The Brooklyn Bridge, an engineering marvel of its time, shows its age through the cracks in the masonry vaults that support the bridge's roadway over Manhattan. Fiber-optic sensors monitor these cracks, as well as other indicators such as temperature fluctuation, to assist structural engineers in determining when the vaults will ultimately need to be replaced. Further up the East River, on the Williamsburg Bridge, a series of interferometric and fiber Bragg grating sensors (both capable of measuring light waves) monitor wire deformation and breakage on the span's century-old suspension cables. Rather than make an annual manual inspection, engineers have access to continuous data, which can tell them if an individual strand in one of the bridge's cable is about to break. Read more in "How to Save America's Crumbling Bridges" by Stephen Goldsmith.

The Santa Clara Valley Water District in Santa Clara County, CA manages a network of natural and man-made infrastructure that supplies 1.8 million residents with water. In an effort to go paperless, district field staff was armed with GIS tablets to survey waterway infrastructure, cataloging and assessing the condition of levees and other assets. These data are now fed back into the district’s asset management software, allowing the agency to not only see infrastructure conditions but to make smart decisions about future investments. According to Esri, more than 4,000 paperless inspections have been processed since 2012. Read more in "Open Data's Road to Better Transit," by Stephen Goldsmith.

The City of Baltimore has one of the most highly regarded urban forests in the United States. But maintaining the net tree canopy, and the myriad positive side effects of urban greenery, requires a sophisticated understanding of tree location, health, and ownership. Old satellite imagery was imprecise and couldn't be easily mapped with other city data, but in 2006 the US Forest Service worked with the University of Vermont’s spatial analysis lab to create a novel land cover map of Baltimore.This new map combined aerial imagery, high-resolution landowner data, and light-reflecting technology to provide a closer view of the city's trees. Scientists and policy-makers can now overlay other computerized records – like data on health, crime, and property ownership – with the urban forest maps to guide tree maintenance, removal, and re-planting. 

The Green City, Clean Waters program in Philadelphia is a city-wide low-impact development approach to mitigating the city’s combined sewer overflows (CSO). The program integrates very low-tech interventions like rain barrels and street trees with very high-tech data collection and analysis. The city is addressing a problem faced by many others that began before the 1950s: a CSO system wherein there is no physical division between stormwater and the sewer system responsible for wastewater coming from homes and businesses. This means when a big storm rolls in, the system becomes overwhelmed, stormwater and wastewater mix, and a toxic effluent is discharged into waterways, degrading the environmental quality for both plants and animals as well as citizens who may live or recreate near these rivers and streams. Luckily, big data analysis and a profusion of sensors spread within the city’s sewer system provide this vital piece of the puzzle, lending some big-technology insights to what is a purposefully low-tech, low-impact approach to attacking the CSO problem. Since the program’s conception, an extensive and quantitative evaluation plan has been in place. Philadelphia pulls data from sensors throughout the system (originally purposed just to warn departments and citizens of overflows) to see if the approach is really working, and also conducts health quality tests in various bodies of water to check if there is a substantive long-term impact. Real cost comparisons can be made between different elements of the program, allowing the city to adjust its plans over time and maximize the returns of each program dollar spent. Read more in "Low-Tech Solutions Meet Data Analytics in Philadelphia's CSO Approach" by Benjamin Weinryb Grohsgal.

Elsewhere, Chicago has recently begun an initiative to use sensors to manage stormwater. In a pilot project, underground sensors in test areas are collecting data on stromwater runoff to enable targeted depolyment of green infrastructure. Read more in "How a Smart City Tackles Rainfall" by Sean Thornton.

Typically, infrastructure is built for able-bodied people, but smart infrastructure and data insights can help city planners and officials build for residents who are differently-abled and/or have mobility challenges. This can include building an "age-friendly smart city," or using unbiased data to build an innovative and inclusive city for all. 

Boston's Mayor's Office of New Urban Mechanics created a crowd-sourcing mobile app called Street Bump that helps residents improve their neighborhood streets by collecting road condition data while they drive. With StreetBump, citizen phones can report rough stretches of road to the City automatically as they drive over them, providing the City of Boston with a useful and cost-effective way of identifying which of its streets need work. Read more in "Beyond 311" by Stephen Goldsmith.

Electric vehicles, or EVS, are increasing in popularity in the United States, but one hesitation for potential consumers is the lack of charging infrastructure. Recent research from Ohio State University is helping local government leaders make informed decisions about EV infrastructure, using data to guide where charging stations should be located, based on route data from the Ohio metropolitan area. 

Cities have begun using digital monitoring techniques for roads in order to enable more frequent and accurate assessment of infrastructure quality. Some, like Cincinnati, have pursued vehicle-based monitoring, equipping vehicles with cameras, lasers, and sensors that identify road surface issues and create georeferenced images. Other cities, including Boston, have turned to smartphone apps that use devices’ cameras and accelerometers to track road quality. These innovations allow cities to identify road quality issues and target interventions before these problems become major pavement failures. Read more in “Sensors and Smartphones: Technological Solutions for Monitoring Road Conditions” by Paul Lillehaugen.

UPS has a program in place called ORION—the On-Road Integrated Optimization and Navigation program — a sophisticated algorithm that ensures UPS vehicles take the most time- and energy-efficient routes. The program saves the company millions of miles each year, which adds up to hundreds of millions in savings and extreme reductions in CO2 emissions. The key to the program’s success is combining computational cost-cutting with functional, driver-friendly goals, like maintaining consistent routes and delivery times. This program provides a viable model for cities looking to improve routes for garbage trucks or other city vehicles. Cities including Boston, Philadelphia, and Raleigh, N.C. have begun taking steps towards route optimization for trash trucks, deploying smart bins that notify the appropriate agency when full to inform routes. Pairing this data with information on how busy a street is at a given time of day, how much a given neighborhood recycles, or where the trash goes after it's collected could make routes much more efficient. Read more in Stephen Goldsmith’s article “What a Brown Delivery Truck Could Teach Government."

Often called "disaster hardening," the process of improving or repairing infrastructure - in a timely manner - helps prevent catastrophic damage from major storms, droughts, or other natural disasters that are intensified by climate change. This is common is places like the Netherlands, where mitigating flood risks is done by advanced preparation and care for infrastructure, rather than repairing them only once major damage has occurred to dams and dikes. Billion dollar climate change and weather disasters are costly for state, local, and federal governments, as outlined by the National Centers for Environmental Information. Timely infrastructure updates, a focus on "green" building, and conducting maintenance instead of deferring will create job opportunities as well as protect people and place from serious future damage.

Washington, D.C.'s Urban Forestry Administration is exploring a model combining lidar and elevation data to find the best places to strategically plant trees to mitigate stormwater. Read more in "How D.C. Grew a Data-Driven Tree Strategy" by Stephen Goldsmith.

Many people who don’t recycle require additional information and messaging about the importance of recycling, proper recycling hygiene, and the importance of disposing of recyclables properly. Some information about recycling is universal, but many municipalities and states have their own specific regulations and methods; it is imperative that local and state governments have consistent, clear messaging so that residents are not only educated about recycling but are able to recycle correctly. The C40 has a helpful guide for cities on boosting recycling rates, which covers the importance of messaging but also touches upon utilizing data to set recycling goals, implementing things like door-to-door collection or material drop-offs, and incentivizing residents.

In 2013, San Francisco began operating a real-time, web-based case management system across the the Departments of Public Health (DPH), Juvenile Probation (JPD), and the Human Services Agency (HSA)  to systematically identify at-risk youth that were clients of multiple city social services. Together, these agencies found that “Crossover clients” of multiple systems were at strikingly increased risk of committing a serious crime. 51 percent of San Franciscans involved in multiple service systems were convicted of a serious crime; a third had been served by all three agencies; and the overwhelming majority (88 percent) of these youth committed the crime more than 90 days after becoming a crossover client – a critical window during which, the analysis suggested, case workers may be able to intervene. Read more in "Getting Data to the Good Guys" by Christopher Kingsley and Stephen Goldsmith.

Alicia Sasser Modestino has spent years studying the impacts of Summer Youth Employment Programs. Her research in Boston found a “30 percent reduction in property crime." This was determined by using arraignment data, and backed up by a similar result in Chicago. According to Modestino, participating in a youth summer job program "reduces the number of arraignments per youth.”

Modestino also had data for a subgroup of youth who were already court-involved, and there were even bigger improvements for that group. The reduction of violent and property crimes was “more like a 50 percent” and there was “a significant improvement in terms of recidivism — they're much less likely to commit future crimes.” 

New Orleans' NOLA for Life campaign analyzes data to determine likelihood of homicide, then targets its campaign components specifically at four neighborhoods where 40 percent of the city's homicides occur despite being home to just 19 percent of New Orleans' residents. And on an even more granular level, the campaign has sought to identify 200 New Orleans students who are most at risk for violence, with the goal of involving them in preventive programs. Read more in "How New Orleans is Winning a War Against Murder" by Stephen Goldsmith. 

Working with the Charlotte-Mecklenburg Police Department in North Carolina in 2015, data scientists from the University of Chicago’s Data Science and Public Policy (DAaPP) developed an Early Intervention System (EIS) to identify individual officers most at-risk for adverse interactions with citizens. The team built a machine learning model that creates a ranked list of officers based on historical data and situational factors that police departments can use to target individual officers for specialized training. This provides a more effective measure for prevention against excessive use of force than simply instituting broad training programs. The DSaPP paper on this machine learning models describes a ranking of past use of force events in historical data with the designations; “not justified, preventable, and sustained.” This data serves as a historical basis for identifying past officers at-risk for future adverse incidents and is combined with other data on dispatch events, criminal complaints, citations, traffic stops, and arrests. The machine learning model “significantly outperformed” the existing within the Charlotte Mecklenburg Police Department used for targeting counseling and training. The model also provided individual risk scores and will more accurately allow the department to allocate resources and reduce unnecessary administrative tasks. The data can also be used at the dispatch level, allowing a dispatcher to recognize an officer that may be less suitable for a specific call because of the associated risk score for adverse citizen interactions.

Accurate statistics around interpersonal (domestic) violence are incredibly difficult to obtain. However, in 2009 the US Department of Justice released "Practical Implications of Current Domestic Violence Research", a seminal report that provided data and education for law enforcement, prosecutors, and judges. This report identified several risk factors for abusers, including a large section on perpetrator characteristics. These included:

1. Gender - the vast majority of perpetrators are male.
2. Age - the median age of an abuser is 33 years old.
3. Prior Record - although the data varies by state, in most places abusers had a prior record and/or were known to police. Having a prior record at all can also indicate likely hood of reabuse.
4. Reabusers - Approximately 1/3 of abusers will reabuse in the short term, and more will reabuse in the long term. Studies show that for abusers who reoffend, the majority do so relatively quickly (within 2-6 months).

With this data, and other sources like the Office of Victims of Crime Intimate Partner Violence guide, local government and law enforcement can begin to understand which offenders are most at risk of committing interpersonal (domestic) violence, and know how to respond to and prevent these crimes. 

In 2006, violent re-offenders established Philadelphia as one of the murder capitals of the United States. Philadelphia’s Adult Probation and Parole Department (APPD) oversaw 50,000 individuals, with only 295 probation officers. To manage the escalating crime, the APPD needed a systematic way of identifying the riskiest individuals and dedicating staff resources accordingly. If the APPD could accurately categorize recently paroled individuals as low-, medium-, or high-risk for potential to commit violent crime, the agency could save time and money and reduce the likelihood of violent recidivism. They turned to sociologist Richard Berk, who built a predictive engine based on tens of thousands of individual criminal records, with dozens of variables such as age, gender, previous zip code, number of previous crimes, and type of offense. This intelligent, machine-learning model enables the computer to find patterns and relationships across dozens of variables and constantly reassess those relationships as new data is added. Read more in "Predictive Tools for Public Safety" by Stephen Goldsmith.

There is no accurate and reliable data about the crisis of murder and disappearance faced by Indigenous women and girls. This is due to a combination of poor data integrity, historic racism against Native communities, and a lack of protocol for these cases. However, it is possible to make changes - particularly to data practices - that would improve record keeping and help establish data-driven policies. This article, with advice from Indigenous experts, outlines steps that cities can take to address the crisis. 

The most effective services for justice-involved youth are ones that happen both before and after release. Research by the Office of Juvenile Justice and Delinquency Prevention found that cognitive-behavior therapy, programs targeting youth in the 16-24 age range, and individual treatment all helped prevent recidivism. The report specifically mentioned Project BUILD in Cook County, Ill, Operation New Hope, and the Wayne County (Michigan) Second Chance Reentry Program.

The Centers for Disease Control and Prevention produced a data-driven guide for emergency managers called “Planning for an Emergency: Strategies for Identifying and Engaging At-Risk Groups,” which outlines how to identify vulnerable populations and how to best assist them during a crisis. The guidelines recommend working with the communities on preparedness planning, responding to a crisis with targeted data (that can easily be used by first responders), planning recovery around previously-identified subpopulations, and mitigating future disasters with data-driven policies.

“Virtual Power Outage Detection Using Social Sensors” is a 2015 working paper that studies how social media can detect power outages. Through an analysis of Twitter and other social media platforms, searching for data and tweets related to a power outage, researchers proved that this type of social media mining was effective in identifying outages. Additionally, the site PowerOutage.US “collects, records, and aggregates live power outage data” and visualizes the information on a map of the United States. The site’s data comes from utility companies rather than consumers, but taken together these two types of data can produce a very comprehensive picture of power outages. Local emergency services could take advantage of the data to coordinate responses and provide assistance to those without power.

The Data-Driven Justice Initiative started as a White House mission in 2015 to break the “cycle of incarceration” and as many as 67 cities, counties, and states signed on to join the bipartisan effort. The municipalities agreed to install innovative solutions to divert potential incarcerees to mental illness or substance abuse treatment, using data analytics to target the individuals most in need of services.

The Data Science and Public Policy team (DSaPP) at the University of Chicago created an accurate Early Intervention System (EIS) that efficiently identifies individuals at risk of contact with the justice system so that the appropriate local agencies that employ this model can provide the necessary services and interventions. The data-driven EIS analyzes a jurisdiction’s mental health, justice, emergency medical response, and social services data to identify these individuals.In 2015, DSaPP partnered with Johnson County, Kansas to install a prototype EIS, combining data from the county’s mental health services, criminal justice system, and local EMS. The data scientists at DSaPP generated a list of 200 people most at risk of coming into contact with the criminal justice system that equipped county officials with a prioritized group most in need of intervention so as to save money from jail costs and reinvest those funds to bolster preventative measures.